{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T18:58:09Z","timestamp":1793559489673,"version":"4.5.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>In this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>According to our results, Gradient Boosting performed best overall, with an AUC &amp;gt;0.8, an accuracy &amp;gt;90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2022.937471","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T05:49:18Z","timestamp":1660110558000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms"],"prefix":"10.3389","volume":"10","author":[{"given":"Cheng-Mao","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiong","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian-Jun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1213\/01.ANE.0000122634.69923.67","article-title":"Prediction of difficult tracheal intubation in thyroid surgery","volume":"99","author":"Bouaggad","year":"2004","journal-title":"Anesth Analg."},{"key":"B2","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1213\/01.ane.0000237305.02465.ee","article-title":"Difficult intubation in thyroid surgery: myth or reality?","volume":"103","author":"Amathieu","year":"2006","journal-title":"Anesth Analg."},{"key":"B3","doi-asserted-by":"publisher","first-page":"169","DOI":"10.4314\/ejhs.v28i2.8","article-title":"Goitre-related factors for predicting difficult intubation in patients scheduled for thyroidectomy in a resource-challenged health institution in North Central Nigeria","volume":"28","author":"Olusomi","year":"2018","journal-title":"Ethiop J Health Sci."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1038\/s41598-021-81188-6","article-title":"A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation","volume":"11","author":"Zhou","year":"2021","journal-title":"Sci Rep."},{"key":"B5","doi-asserted-by":"publisher","first-page":"655686","DOI":"10.3389\/fmed.2021.655686","article-title":"Developing machine learning algorithms to predict pulmonary complications after emergency gastrointestinal surgery","volume":"8","author":"Xue","year":"2021","journal-title":"Front Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"110423","DOI":"10.1016\/j.jclinane.2021.110423","article-title":"Predicting chronic pain in postoperative breast cancer patients with multiple machine learning and deep learning models","volume":"74","author":"Wang","year":"2021","journal-title":"J Clin Anesth."},{"key":"B7","doi-asserted-by":"publisher","first-page":"e8583","DOI":"10.7717\/peerj.8583","article-title":"A comparative study of machine learning algorithms for predicting acute kidney injury after liver cancer resection","volume":"8","author":"Lei","year":"2020","journal-title":"PeerJ."},{"key":"B8","doi-asserted-by":"publisher","first-page":"110278","DOI":"10.1016\/j.jclinane.2021.110278","article-title":"Constructing a prediction model for difficult intubation of obese patients based on machine learning","volume":"72","author":"Zhou","year":"2021","journal-title":"J Clin Anesth."},{"key":"B9","doi-asserted-by":"publisher","first-page":"103530","DOI":"10.1016\/j.bspc.2022.103530","article-title":"Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients","volume":"74","author":"Ayalew","year":"2022","journal-title":"Biomed Signal Process Control."},{"key":"B10","doi-asserted-by":"publisher","first-page":"308","DOI":"10.4097\/kjae.2013.64.4.308","article-title":"The causes of difficult tracheal intubation and preoperative assessments in different age groups","volume":"64","author":"Moon","year":"2013","journal-title":"Korean J Anesthesiol."},{"key":"B11","doi-asserted-by":"publisher","first-page":"164","DOI":"10.4103\/jpgm.JPGM_545_18","article-title":"Validation of age and height based formulae to predict paediatric airway distances - 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